class_docstr: Class for complement graphs.
  
  The main goal is to be able to work with big and dense graphs with
  a low memory footprint.
  
  In this class you add the edges that *do not exist* in the dense graph,
  the report methods of the class return the neighbors, the edges and
  the degree as if it was the dense graph. Thus it's possible to use
  an instance of this class with some of NetworkX functions.
docstr: Return an iterator of (node, adjacency set) tuples for all nodes
     in the dense graph.
  
  This is the fastest way to look at every edge.
  For directed graphs, only outgoing adjacencies are included.
  
  Returns
  -------
  adj_iter : iterator
     An iterator of (node, adjacency set) for all nodes in
     the graph.
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code: def adjacency(self):
      """Return an iterator of (node, adjacency set) tuples for all nodes
         in the dense graph.
  
      This is the fastest way to look at every edge.
      For directed graphs, only outgoing adjacencies are included.
  
      Returns
      -------
      adj_iter : iterator
         An iterator of (node, adjacency set) for all nodes in
         the graph.
      """
      nodes = set(self.adj)
      for n, nbrs in self.adj.items():
          yield (n, nodes - set(nbrs) - {n})
  
file_path: networkx/examples/subclass/plot_antigraph.py
imports: import matplotlib.pyplot
  import networkx
  from networkx import Graph
label_desc: Use image node_id 5 for calling the AntiGraph obj's underlying member method code with example usage: obj.adjacency() without return types